Generating near-infrared facial expression datasets with dimensional affect labels
This work addresses the problem of illumination variance and limited emotion representation in facial expression analysis for computer vision researchers, though it is incremental as it builds on existing augmentation techniques.
The authors tackled the lack of near-infrared facial expression datasets with dimensional emotion labels by proposing two data augmentation methods, face morphing and CycleGAN, to generate such datasets from existing categorical or visible-light sources, resulting in datasets comparable in quality and baseline performance to existing ones.
Facial expression analysis has long been an active research area of computer vision. Traditional methods mainly analyse images for prototypical discrete emotions; as a result, they do not provide an accurate depiction of the complex emotional states in humans. Furthermore, illumination variance remains a challenge for face analysis in the visible light spectrum. To address these issues, we propose using a dimensional model based on valence and arousal to represent a wider range of emotions, in combination with near infra-red (NIR) imagery, which is more robust to illumination changes. Since there are no existing NIR facial expression datasets with valence-arousal labels available, we present two complementary data augmentation methods (face morphing and CycleGAN approach) to create NIR image datasets with dimensional emotion labels from existing categorical and/or visible-light datasets. Our experiments show that these generated NIR datasets are comparable to existing datasets in terms of data quality and baseline prediction performance.